Assessment and diagnosis are essential to mental health research and treatment, from informing treatment decisions and monitoring symptoms over time, to identifying population trends in prevalence and developing evidence-informed health policy. Yet the existing psychiatric classifications that guide diagnosis in mental health are fraught with poor reliability, validity, and utility. Extensive research has shown that these diagnostic criteria fail to adequately capture the nature and experience of people with mental and substance use disorders.
To achieve priority goals in mental health research, there is a critical need to develop data-driven ways to think about mental and substance use disorders and develop precise assessment tools that map onto these new ideas. Without precise methods to assess mental disorders and individual consumer needs, clinical services are constrained in their ability to identify the most suitable treatment strategies and appropriately adapt to varied patient response.
Researchers at the Matilda Centre with collaborators from Macquarie University and the Australian National University, in partnership with a large international consortium (the Hierarchical Taxonomy of Psychopathology), have led a program of research that utilises large-scale general population data and longitudinal cohort studies to develop new approaches that better conceptualise, organise, and describe the broad and specific experience of mental and substance use disorders (often referred to as the “structure” of mental and substance use disorders).
The project embraces complex statistical techniques and psychometric methods that have been used in other fields, notably educational and psychological testing, to push forward the boundaries of psychiatric assessment and diagnosis. To date, we have made significant progress across three areas:
Within these three areas we have empirically demonstrated that the commonalities across mental and substance use disorders can be better conceptualised as a smaller number of broad variables. For example, mood and anxiety disorders may better reflect a broad experience of “internalising” disorders, whereas substance use, sensation seeking, impulsiveness, and disinhibition may better reflect a broad experience of “externalising” disorders.
Importantly, we have shown that measuring mental health and substance use in this way can better explain associations between individual disorders and other clinically relevant factors, such as suicidality, poor sleep, low self-esteem, risky sexual behaviours, physical health conditions, maladaptive personality, lifestyle risk behaviours, and traumatic experiences, than using traditional categorical diagnosis.
As such, there are several new self-report measures that target internalising, externalising, and other broad factors currently in development and demonstrated promising psychometric properties. To further advance this field, we have proposed and have previously demonstrated the feasibility of using techniques such as computerised adaptive testing to measure internalising and externalising in a highly efficient yet precise manner. Moving forward these tests could be used in routine clinical settings to better target and tailor interventions to individual patient needs.
Matthew Sunderland (University of Sydney), Tim Slade (University of Sydney), Miriam Forbes (Macquarie University), Samantha Lynch (Université de Montréal), Louise Mewton (University of Sydney), Philip Batterham (Australian National University), Alison Calear (Australian National University), Natacha Carragher (UNSW), Andrew Baillie (University of Sydney), Maree Teesson (University of Sydney), Martin Sellbom (University of Otago) Robert Krueger (University of Minnesota), David Watson (University of Notre Dame), Roman Kotov (Stony Brook University), the HiTOP Consortium (multiple institutions).